Education and labor mobility are key drivers in the production of human capital, fundamental to economic development. In the U.S., the varying skill production efficiencies of state-specific education systems and the dynamics of worker migration shape human capital of states, influencing economic outcomes at both the state and national levels. This paper develops a novel dynamic spatial general equilibrium model with overlapping generation framework in which heterogeneous individuals accumulate human capital and move across states. Calibrated to the U.S. economy, the model illustrates how variations in education efficiency lead to substantial cross-state income disparities and shows that internal migration can notably boost output in states with lower education efficiencies. At the national level, free mobility of workers yields a 6.9% output gain. Moreover, the model suggests that variations in human capital account for 46.6% of the state variation per capita output. Applying the calibrated model to analyze the Obama Administration's Race to the Top initiative finds that the $4.1 billion grant spurred a 0.2% increase in U.S. GDP, mostly benefiting the grant-winning states and their neighbors. Additionally, alternative grant allocation experiments show that strategic reallocation of education grants, considering state skill production efficiencies, could further increase national GDP gains without necessarily worsening state income disparities.

Many economic shocks affect not only workers’ wages and employment but also health. We study their mechanism, welfare effects, and policy implications in a dynamic quantitative model with endogenous health investment. The health production technology is flexible, allowing workers to optimally choose to forego or partially treat sickness, or incur non-medical health investment. Applying this model to the China shock, we estimate its causal effects on health and calibrate the model to the pre-China shock economy. Our simulations suggest that, first, the health investment mechanism is economically significant, with the model elasticity accounting for between 40-50% of the empirical estimates. Second, the workers’ steady-state welfare loss is equivalent to an annual drop of 8.4% in consumption, and both endogenous health investment and health itself play important roles. Finally, while universal health insurance is effective in reducing partial or foregone treatment, it offers little protection for non-medical investment. Therefore, its overall efficacy would be nuanced.

This paper explores the effect of two different types of land use policies on regional economies by using regional level data of South Korea. We analyze the effect of conventional land-use restrictions in existing cities as well as the effect of unique land-supply policies, motivated by the South Korean government’s 2nd New Town Project which built new cities from the scratch, supplying 666,000 houses near Seoul Metropolitan Area (SMA). We estimate the effect of such policies on the aggregate and regional economies, considering both the efficiency gain from the resource reallocation and externalities from regional decline. If the government introduces tighter land-use restrictions in SMA and relaxes land-use restrictions in other regions, regional population will more evenly distribute at the cost of aggregate GDP loss and universal housing price increases. Our back-of-the-envelope calculation indicates that the 2nd New Town Project was cost effective as it permanently increased steady state real aggregate GDP flow by 0.4%, for the one-time cost amounting 4.05% of GDP. It however exacerbated regional decline by decreasing overall rural population share by 4%.

Compulsory public education is a common policy tool to achieve greater participation in education, particularly from marginalized groups. The COVID-19 pandemic brought unprecedented school closures around the globe. Households seek their own mitigation strategy against education loss via private substitutes. We use the COVID-19 response of South Korea as a laboratory for investigating the effects of pandemic-induced school closure on parental investment and educational outcomes. Specifically, we exploit the school-level variations in in-person learning days driven by government and local authorities. Combining administrative data and longitudinal survey, our fixed effects regression identifies that losing 10 additional days implies a 9.5% increase in private education expenditure. In addition, we build and estimate a structural model tournament to quantify the impact of school closure on educational inequalities. A counterfactual experiment suggests that a $450 voucher for private tutoring may compensate for the test score loss caused by a 20-day loss of in-person schooling. Our results shed light on how households make schooling decisions and have implications on the longer-run educational inequality induced by the pandemic. Methodologically, we propose an estimation algorithm that tightly links the natural experiment estimates and the structural model.